雜志介紹

Journal Of Magnetic Resonance雜志介紹

《Journal Of Magnetic Resonance》是一本以English為主的未開放獲取國(guó)際優(yōu)秀期刊，中文名稱磁共振雜志，本刊主要出版、報(bào)道化學(xué)-SPECTROSCOPY領(lǐng)域的研究動(dòng)態(tài)以及在該領(lǐng)域取得的各方面的經(jīng)驗(yàn)和科研成果，介紹該領(lǐng)域有關(guān)本專業(yè)的最新進(jìn)展，探討行業(yè)發(fā)展的思路和方法，以促進(jìn)學(xué)術(shù)信息交流，提高行業(yè)發(fā)展。該刊已被國(guó)際權(quán)威數(shù)據(jù)庫(kù)SCIE收錄，為該領(lǐng)域相關(guān)學(xué)科的發(fā)展起到了良好的推動(dòng)作用，也得到了本專業(yè)人員的廣泛認(rèn)可。該刊最新影響因子為2，最新CiteScore 指數(shù)為3.8。

本刊近期中國(guó)學(xué)者發(fā)表的論文主要有：

• A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning

  Author: Luo, Gang; Xiao, Lizhi; Luo, Sihui; Liao, Guangzhi; Shao, Rongbo

• Multiple acquisitions in a single scan: exhausting abundant( 1)H polarization at fast MAS

  Author: Yan, Zhiwei; Zhang, Rongchun

• A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction

  Author: Guo, Di; Zeng, Gushan; Fu, Hao; Wang, Zi; Yang, Yonggui; Qu, Xiaobo

• Probing distribution and dynamics of lithium ions in supermolecule p-CD-PEO/Li plus solid polymer electrolytes via solid-state NMR

  Author: Song, Yi-Dan; Sun, Jianchao; Fu, Xiao-Bin; Yao, Ye-Feng

英文介紹

Journal Of Magnetic Resonance雜志英文介紹

The Journal of Magnetic Resonance presents original technical and scientific papers in all aspects of magnetic resonance, including nuclear magnetic resonance spectroscopy (NMR) of solids and liquids, electron spin/paramagnetic resonance (EPR), in vivo magnetic resonance imaging (MRI) and spectroscopy (MRS), nuclear quadrupole resonance (NQR) and magnetic resonance phenomena at nearly zero fields or in combination with optics. The Journal's main aims include deepening the physical principles underlying all these spectroscopies, publishing significant theoretical and experimental results leading to spectral and spatial progress in these areas, and opening new MR-based applications in chemistry, biology and medicine. The Journal also seeks descriptions of novel apparatuses, new experimental protocols, and new procedures of data analysis and interpretation - including computational and quantum-mechanical methods - capable of advancing MR spectroscopy and imaging.